The size of the pupil has a large effect on visual function, and pupil size depends mainly on the adapting luminance, modulated by other factors. Over the last century, a number of formulas have been proposed to describe this dependence. Here we review seven published formulas and develop a new unified formula that incorporates the effects of luminance, size of the adapting field, age of the observer, and whether one or both eyes are adapted. We provide interactive demonstrations and software implementations of the unified formula.

A Unified Formula For Light-adapted Pupil Size

FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception. While many other metrics are available, our work provides the first practical treatment of these three central aspects of vision simultaneously. The complex interplay between spatial and temporal sensitivity across retinal locations is especially important for displays that cover a large field-of-view, such as Virtual and Augmented Reality displays, and associated methods, such as foveated rendering. Our metric is derived from psychophysical studies of the early visual system, which model spatio-temporal contrast sensitivity, cortical magnification and contrast masking. It accounts for physical specification of the display (luminance, size, resolution) and viewing distance. To validate the metric, we collected a novel foveated rendering dataset which captures quality degradation due to sampling and reconstruction. To demonstrate our algorithm's generality, we test it on 3 independent foveated video datasets, and on a large image quality dataset, achieving the best performance across all datasets when compared to the state-of-the-art.

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FovVideoVDP: a visible difference predictor for wide field-of-view video

A fundamental problem in extracting scene structure is distinguishing different physical sources of image structure. Light reflected by an opaque surface covaries with local surface orientation, whereas light transported through the body of a translucent material does not. This suggests the possibility that the visual system may use the covariation of local surface orientation and intensity as a cue to the opacity of surfaces. We tested this hypothesis by manipulating the contrast of luminance gradients and the surface geometries to which they belonged and assessed how these manipulations affected the perception of surface opacity/translucency. We show that (i) identical luminance gradients can appear either translucent or opaque depending on the relationship between luminance and perceived 3D surface orientation, (ii) illusory percepts of translucency can be induced by embedding opaque surfaces in diffuse light fields that eliminate the covariation between surface orientation and intensity, and (iii) illusory percepts of opacity can be generated when transparent materials are embedded in a light field that generates images where surface orientation and intensity covary. Our results provide insight into how the visual system distinguishes opaque surfaces and light-permeable materials and why discrepancies arise between the perception and physics of opacity and translucency. These results suggest that the most significant information used to compute the perceived opacity and translucency of surfaces arise at a level of representation where 3D shape is made explicit.

https://www.pnas.org/content/pnas/114/52/13840.full.pdf

Perception and misperception of surface opacity.

Standard image quality metrics, such as PSNR or SSIM, cannot be directly computed on linear high dynamic range colour values because such values non-linearly related to our perception of visible differences. In this work, we develop a new encoding function (PU21) to convert absolute high dynamic range (HDR) linear colour values into approximately perceptually uniform (PU) values, which can be used with standard quality metrics. The proposed PU21 function is based on a recent contrast sensitivity model, fitted to the measurements up to 10000cd/m2. Unlike the conventional simplified approach of deriving PU functions based on peak sensitivities, we model realistic coding artefacts to find visibility thresholds for our derivation. Furthermore, the new PU accounts for the effect of glare on image quality. The proposed PU21 improves the accuracy of quality predictions for standard metrics of PSNR, VSI, FSIM, SSIM, and MS-SSIM in their correlation with subjective scores on HDR images included in UPIQ, one of the largest HDR image quality datasets.

PU21: A novel perceptually uniform encoding for adapting existing quality metrics for HDR

When judging the optical properties of a translucent object, humans often look at sharp geometric features such as edges and thin parts. An analysis of the physics of light transport shows that these sharp geometries are necessary for scientific imaging systems to be able to accurately measure the underlying material optical properties. In this article, we examine whether human perception of translucency is likewise affected by the presence of sharp geometry, by confounding our perceptual inferences about an object's optical properties. We use physically accurate simulations to create visual stimuli of translucent materials with varying shapes and optical properties under different illuminations. We then use these stimuli in psychophysical experiments, where human observers are asked to match an image of a target object by adjusting the material parameters of a match object with different geometric sharpness, lighting, and three-dimensional geometry. We find that the level of geometric sharpness significantly affects perceived translucency by observers. These findings generalize across a few illumination conditions and object shapes. Our results suggest that the perceived translucency of an object depends on both the underlying material's optical parameters and the three-dimensional shape of the object. We also find that models based on image contrast cannot fully predict the perceptual results.

Effect of geometric sharpness on translucent material perception

Contrast sensitivity functions (CSFs) characterize the sensitivity of the human visual system at different spatial scales, but little is known as to how contrast sensitivity for achromatic and chromatic stimuli changes from a mesopic to a highly photopic range reflecting outdoor illumination levels. The purpose of our study was to further characterize the CSF by measuring both achromatic and chromatic sensitivities for background luminance levels from 0.02 cd/m2 to 7,000 cd/m2. Stimuli consisted of Gabor patches of different spatial frequencies and angular sizes, varying from 0.125 to 6 cpd, which were displayed on a custom high dynamic range (HDR) display with luminance levels up to 15,000 cd/m2. Contrast sensitivity was measured in three directions in color space, an achromatic direction, an isoluminant "red-green" direction, and an S-cone isolating "yellow-violet" direction, selected to isolate the luminance, L/M-cone opponent, and S-cone opponent pathways, respectively, of the early postreceptoral processing stages. Within each session, observers were fully adapted to the fixed background luminance (0.02, 2, 20, 200, 2,000, or 7,000 cd/m2). Our main finding is that the background luminance has a differential effect on achromatic contrast sensitivity compared to chromatic contrast sensitivity. The achromatic contrast sensitivity increases with higher background luminance up to 200 cd/m2 and then shows a sharp decline when background luminance is increased further. In contrast, the chromatic sensitivity curves do not show a significant sensitivity drop at higher luminance levels. We present a computational luminance-dependent model that predicts the CSF for achromatic and chromatic stimuli of arbitrary size.

Spatio-chromatic contrast sensitivity under mesopic and photopic light levels

Lightness constancy is the ability to perceive black and white surface colors under a wide range of lighting conditions. This fundamental visual ability is not well understood, and current theories differ greatly on what image features are important for lightness perception. Here we measured classification images for human observers and four models of lightness perception to determine which image regions influenced lightness judgments. The models were a high-pass-filter model, an oriented difference-of-Gaussians model, an anchoring model, and an atmospheric-link-function model. Human and model observers viewed three variants of the argyle illusion (Adelson, 1993) and judged which of two test patches appeared lighter. Classification images showed that human lightness judgments were based on local, anisotropic stimulus regions that were bounded by regions of uniform lighting. The atmospheric-link-function and anchoring models predicted the lightness illusion perceived by human observers, but the high-pass-filter and oriented-difference-of-Gaussians models did not. Furthermore, all four models produced classification images that were qualitatively different from those of human observers, meaning that the model lightness judgments were guided by different image regions than human lightness judgments. These experiments provide a new test of models of lightness perception, and show that human observers' lightness computations can be highly local, as in low-level models, and nevertheless depend strongly on lighting boundaries, as suggested by midlevel models.

/pdf/what-image-features-guide-lightness-perception-1lpqqtumqo.pdf

What image features guide lightness perception

We model color contrast sensitivity for Gabor patches as a function of spatial frequency, luminance and chromacity of the background, modulation direction in the color space and stimulus size. To fit the model parameters, we combine the data from five independent datasets, which let us make predictions for background luminance levels between 0.0002 cd/m2and 10 000 cd/m2, and for spatial frequencies between 0.06 cpd and 32 cpd. The data are well-explained by two models: a model that encodes cone contrast and a model that encodes postreceptoral, opponent-color contrast. Our intention is to create practical models, which can well explain the detection performance for natural viewing in a wide range of conditions. As our models are fitted to the data spanning very large range of luminance, they can find applications in modeling visual performance for high dynamic range and augmented reality displays.

Practical Color Contrast Sensitivity Functions for Luminance Levels up to 10000 cd/m2

Perceived three-dimensional shape toggles perceived glow

Contrast sensitivity functions (CSFs) characterize the sensitivity of the human visual system at different spatial frequencies. However, little is known about CSFs at luminances above 1000 cd/m2, especially for color. Here, we measured contrast sensitivities at background luminances from 0.02 cd/m2 to 7000 cd/m2 and for three color directions (black-white or achromatic, red-green, and yellow-violet). Stimuli were Gabor patches of various spatial frequencies (0.125 to 6 cpd), displayed on a high dynamic range display (peak luminance: 15,000 cd/m2). We found that achromatic contrast sensitivity has an inverted Ushape as a function of background luminance, with peak sensitivity at 200 cd/m2, while red-green and yellow-violet contrast sensitivities were monotonic functions of background luminance, saturating at 200 cd/m2. Based on these measurements, we developed a model that predicts contrast sensitivity for the average observer. This model is intended for applications in high dynamic range imaging. Introduction Spatial vision refers to the ability to see variations of image intensity across space; it is one of the basic elements in our understanding of human vision. Existing work has largely focused on stimulus visibility as a function of spatial frequency [4, 18, 7, 14, 16, 13, 3]. A typical experiment measures the minimum contrast required to detect a target stimulus (contrast threshold), which indicates the sensitivity of the visual system to that spatial frequency (contrast sensitivity); the value of contrast sensitivity as a function of spatial frequency is known as the contrast sensitivity function (CSF). See [20] for a model and comprehensive review of achromatic contrast detection. However, little is known about contrast sensitivity at very high and very low luminance levels. For achromatic contrast, measurements exist for luminance up to approximately 1000 cd/m2 [19, 12]; no measurements exist for color contrast at such extreme levels. Here, we describe contrast sensitivity over a wide range of frequencies, colors, and luminances. We also present a computational model of contrast sensitivity for an average (standard) observer. As such, our data and model together inform how the visual system operates at the very high and low luminance levels that high-dynamic-range (HDR) displays can reach. See [23] for a more detailed description of our work, including additional experiments. Contrast Detection Experiment We measured contrast thresholds for target stimuli with in three color directions and at luminances ranging from 0.02 cd/m2 (low mesopic) to 7000 cd/m2 (high photopic). Stimuli The stimuli were Gabor patches created by multiplying a Gaussian envelope with a sinusoidal grating centered at the peak of the Gaussian (Fig. 1). The gratings were of spatial frequencies f = 0.5, 1, 2, 4, or 6 cycles per degree of visual angle (cpd), and the width of the Gaussian envelope was σ = 0.5 f−1 visual degrees; thus, all stimuli showed the same number of cycles (‘fixed-cycles’), but varied in size as a function of f . Such stimuli allowed us to treat the visible number of cycles, and therefore stimulus size, as an additional parameter for modeling. The Gabors were modulated around a neutral grey (white) that was metameric with D65 (CIE 1931 x, y = 0.3127, 0.3290). Color modulations were defined in DerringtonKrauskopf-Lennie (DKL) space [6], whose cardinal directions correspond to combinations of cone responses: achromatic (L+ M), red-green (L−M), and yellow-violet (S− (L+M)). DKL space allows a device-independent definition of the chromatic stimulus modulations, and thus, comparisons with CSF measurements in literature.

Minjung Kim

Papers

Spatio-chromatic contrast sensitivity under mesopic and photopic light levels

What image features guide lightness perception

Practical Color Contrast Sensitivity Functions for Luminance Levels up to 10000 cd/m2

Perceived three-dimensional shape toggles perceived glow

Contrast Sensitivity Functions for HDR Displays